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Can I run Nemotron 3.5 Bolt E4B on Generic Android Phone (8GB RAM)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Nemotron 3.5 Bolt E4B needs ~8.9 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.

Needs ~8.9 GB Device usable ~4.5 GB

Needs ~8.9 GB even at Q4_K_M, but only ~4.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 4.4 GB: Nemotron 3.5 Bolt E4B needs roughly 8.9 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Nemotron 3.5 Bolt E4B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Nemotron 3.5 Bolt E4B on Nvidia GeForce RTX 3060 (12GB).

Q4_K_M needed
~8.9 GB
Usable on device
~4.5 GB
Device memory
8 GB
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Which quant fits

Quant ladder vs ~4.5 GB usable
Q2_K
~4.9 GB
Q3_K_M
~5.4 GB
Q4_K_M
~8.9 GB
Q5_K_M
~7.2 GB
Q6_K
~8.1 GB
Q8_0
~10.6 GB
FP16
~17.1 GB
The line marks Generic Android Phone (8GB RAM)'s ~4.5 GB budget; rungs past it are too large.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model Nemotron
Parameters
8B
Q4_K_M size
7.37 GB
Q8_0 size
9.09 GB
Context
64k
Ollama tag
nemotron-3.5-bolt:e4b-q4_K_M
Full Nemotron 3.5 Bolt E4B requirements →
Device Android
Memory
8 GB ram
Usable for weights
~4.5 GB
Best runtime
llama.cpp (PocketPal or SmolChat)
Best models for Generic Android Phone (8GB RAM) →

What you can run instead

Run Nemotron 3.5 Bolt E4B on other hardware

FAQ

Can Generic Android Phone (8GB RAM) run Nemotron 3.5 Bolt E4B?

No. Nemotron 3.5 Bolt E4B needs ~8.9 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.

How much memory does Nemotron 3.5 Bolt E4B need?

Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~7.37 GB; with KV cache and runtime overhead, budget ~8.9 GB at a 4k context.

What is the best tool to run Nemotron 3.5 Bolt E4B on Android?

On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.